{"id":"W2895106137","doi":"10.48550/arxiv.1810.02334","title":"Unsupervised Learning via Meta-Learning","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Unsupervised learning; Computer science; Meta learning (computer science); Machine learning; Artificial intelligence; Cluster analysis; Embedding; Construct (python library); Variety (cybernetics); Task (project management); Feature learning; Conceptual clustering; Competitive learning; Fuzzy clustering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003459691,0.001759021,0.00175782,0.001671136,0.0007576051,0.00202623,0.003450535,0.001936634,0.001443968],"category_scores_gemma":[0.01207237,0.001070638,0.002141276,0.00141871,0.002231323,0.003995533,0.00320836,0.003628282,0.0008303789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001531137,"about_ca_system_score_gemma":0.001550798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00150385,"about_ca_topic_score_gemma":0.003081362,"domain_scores_codex":[0.9975388,0.001122227,0.0001197741,0.0007927495,0.000300333,0.000126105],"domain_scores_gemma":[0.993232,0.003987524,0.000436877,0.00161796,0.0005281707,0.0001974714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000158889,0.0002218958,0.003141972,0.00034236,0.0004384108,0.00011516,0.0002469245,0.7421744,0.003939208,0.04904173,0.003694848,0.1964841],"study_design_scores_gemma":[0.000009622385,0.00002755646,0.0001429494,0.00001583635,0.00001552862,0.0000209549,0.00001187058,0.9533325,0.001055906,0.04481358,0.0005427434,0.00001089258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007191187,0.0002775324,0.9910154,0.0002068703,0.00002079933,0.00004783433,0.00009110542,0.0005587203,0.0005906039],"genre_scores_gemma":[0.4056737,0.0006299922,0.5880886,0.0004761403,0.0001741018,0.0005913823,0.001292139,0.0003858797,0.002688094],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003459691,"threshold_uncertainty_score":0.01829684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1250216964980959,"score_gpt":0.1969916416368858,"score_spread":0.0719699451387899,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}